{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://mp.weixin.qq.com/s?__biz=MzkyMDMxNDkxOA==&mid=2247484429&idx=1&sn=d4c6d295c01e66c09e115c9cc9f92346&chksm=c195f144f6e27852a4e09d361c5d2a8cd2d2cb31af3696810da6f3424984cdc9889092090581&scene=178&cur_album_id=3447886516519747585#rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 振动升降指标ASI策略.py\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "from utils.indicators import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hist_k_data(code,start_date,end_date,frequency='d')->pd.DataFrame:\n",
    "    \"\"\"\n",
    "    获取历史K线数据\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    import baostock as bs\n",
    "    bs.login()\n",
    "    rs = bs.query_history_k_data_plus(code,\"date,code,open,high,low,close,preclose,volume,amount,pctChg\",start_date,end_date,frequency=frequency)\n",
    "    data_list = []\n",
    "    while (rs.error_code == '0') & rs.next():\n",
    "        # 获取一条记录，将记录合并在一起\n",
    "        data_list.append(rs.get_row_data())\n",
    "    result = pd.DataFrame(data_list, columns=rs.fields)\n",
    "    result.open = result.open.astype(float)\n",
    "    result.high = result.high.astype(float)\n",
    "    result.low = result.low.astype(float)\n",
    "    result.close = result.close.astype(float)\n",
    "    result.date= pd.to_datetime(result.date)\n",
    "    result.set_index('date',inplace=True)\n",
    "    bs.logout()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "login success!\n",
      "logout success!\n"
     ]
    }
   ],
   "source": [
    "# 获取股票数据\n",
    "stock_symbol = 'sz.000001'\n",
    "start_date = '2005-01-01'\n",
    "end_date = '2023-01-01'\n",
    "data = get_hist_k_data(stock_symbol, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dev\\AppData\\Local\\Temp\\ipykernel_34828\\3602870477.py:13: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  data['Signal'][data['AO'] > 0] = 1\n",
      "C:\\Users\\Dev\\AppData\\Local\\Temp\\ipykernel_34828\\3602870477.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Signal'][data['AO'] > 0] = 1\n",
      "C:\\Users\\Dev\\AppData\\Local\\Temp\\ipykernel_34828\\3602870477.py:15: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  data['Signal'][data['AO'] < 0] = -1\n",
      "C:\\Users\\Dev\\AppData\\Local\\Temp\\ipykernel_34828\\3602870477.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['Signal'][data['AO'] < 0] = -1\n"
     ]
    }
   ],
   "source": [
    "# 计算AO指标\n",
    "def calculate_ao(data):\n",
    "    data['AO'] = data['close'].rolling(window=5).mean() - data['close'].rolling(window=34).mean()\n",
    "    return data\n",
    "\n",
    "# 生成交易信号\n",
    "#data = calculate_ao(data)\n",
    "data =data.calculate_ao()\n",
    "data['Signal'] = 0\n",
    "data['Position'] = 0\n",
    "\n",
    "# 当AO值从负变正时买入\n",
    "data['Signal'][data['AO'] > 0] = 1\n",
    "# 当AO值从正变负时卖出\n",
    "data['Signal'][data['AO'] < 0] = -1\n",
    "\n",
    "# 计算持仓\n",
    "data['Position'] = data['Signal'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 回测策略\n",
    "def backtest_strategy(data):\n",
    "    data['Strategy_Returns'] = data['Position'].shift(1) * data['close'].pct_change()\n",
    "    data['Cumulative_Returns'] = (1 + data['Strategy_Returns']).cumprod()\n",
    "    return data\n",
    "\n",
    "data = backtest_strategy(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制策略的累计收益\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(data['Cumulative_Returns'], label='AO Strategy')\n",
    "plt.title('AO Strategy Cumulative Returns')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cumulative Returns')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
